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Joint sequence optimization beats pure neural network approaches for super-resolution TSE (bibtex)
by HN Dang, V Golkov, J Endres, S Weinmüller, F Glang, T Wimmer, D Cremers, A Dörfler, A Maier and and M Zaiss
Reference:
Joint sequence optimization beats pure neural network approaches for super-resolution TSE (HN Dang, V Golkov, J Endres, S Weinmüller, F Glang, T Wimmer, D Cremers, A Dörfler, A Maier and and M Zaiss), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2024.
Bibtex Entry:
@inproceedings{Dang2024,
title = {Joint sequence optimization beats pure neural network approaches for super-resolution {TSE}},
author = {HN Dang and V Golkov and J Endres and S Weinmüller and F Glang and T Wimmer and D Cremers and A Dörfler and A Maier and and M Zaiss},
year = {2024},
booktitle = {International Society for Magnetic Resonance in Medicine ({ISMRM}) Annual Meeting},
keywords = {MRI, magnetic resonance imaging, medical imaging, pulse sequences, deep learning, neural networks, MRI reconstruction},
}
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